Overview

Brought to you by YData

Dataset statistics

Number of variables48
Number of observations2702
Missing cells21616
Missing cells (%)16.7%
Total size in memory992.3 KiB
Average record size in memory376.0 B

Variable types

Text11
Numeric28
Unsupported8
DateTime1

Alerts

media_and_data_integrity_errors has constant value "0.0"Constant
error_information_log_entries has constant value "0.0"Constant
warning__comp._temperature_time has constant value "0"Constant
critical_comp._temperature_time has constant value "0.0"Constant
nand_type has 2702 (100.0%) missing valuesMissing
optional_features has 2702 (100.0%) missing valuesMissing
log_page_support has 2702 (100.0%) missing valuesMissing
available_spare.1 has 2702 (100.0%) missing valuesMissing
available_spare_threshold has 2702 (100.0%) missing valuesMissing
percentage_used.1 has 2702 (100.0%) missing valuesMissing
data_units_read has 2702 (100.0%) missing valuesMissing
data_units_written has 2702 (100.0%) missing valuesMissing
ieee_oui_identifier has unique valuesUnique
unallocated has unique valuesUnique
nand_type is an unsupported type, check if it needs cleaning or further analysisUnsupported
optional_features is an unsupported type, check if it needs cleaning or further analysisUnsupported
log_page_support is an unsupported type, check if it needs cleaning or further analysisUnsupported
available_spare.1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
available_spare_threshold is an unsupported type, check if it needs cleaning or further analysisUnsupported
percentage_used.1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
data_units_read is an unsupported type, check if it needs cleaning or further analysisUnsupported
data_units_written is an unsupported type, check if it needs cleaning or further analysisUnsupported
percentage_used has 29 (1.1%) zerosZeros
unsafe_shutdowns has 446 (16.5%) zerosZeros
media_and_data_integrity_errors has 2702 (100.0%) zerosZeros
error_information_log_entries has 2702 (100.0%) zerosZeros
warning__comp._temperature_time has 2702 (100.0%) zerosZeros
critical_comp._temperature_time has 2702 (100.0%) zerosZeros

Reproduction

Analysis started2025-12-13 19:18:47.854674
Analysis finished2025-12-13 19:18:48.349136
Duration0.49 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:48.549616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length6
Mean length5.870096225
Min length3

Characters and Unicode

Total characters15861
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHPE
2nd rowInspur
3rd rowFujitsu
4th rowSuperMicro
5th rowInspur
ValueCountFrequency (%)
hpe413
15.3%
supermicro413
15.3%
lenovo407
15.1%
cisco387
14.3%
dell373
13.8%
fujitsu369
13.7%
inspur340
12.6%
2025-12-13T12:18:48.997494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o1614
 
10.2%
u1491
 
9.4%
e1193
 
7.5%
i1169
 
7.4%
r1166
 
7.4%
s1096
 
6.9%
c800
 
5.0%
p753
 
4.7%
n747
 
4.7%
l746
 
4.7%
Other values (13)5086
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15861
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1614
 
10.2%
u1491
 
9.4%
e1193
 
7.5%
i1169
 
7.4%
r1166
 
7.4%
s1096
 
6.9%
c800
 
5.0%
p753
 
4.7%
n747
 
4.7%
l746
 
4.7%
Other values (13)5086
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15861
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1614
 
10.2%
u1491
 
9.4%
e1193
 
7.5%
i1169
 
7.4%
r1166
 
7.4%
s1096
 
6.9%
c800
 
5.0%
p753
 
4.7%
n747
 
4.7%
l746
 
4.7%
Other values (13)5086
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15861
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1614
 
10.2%
u1491
 
9.4%
e1193
 
7.5%
i1169
 
7.4%
r1166
 
7.4%
s1096
 
6.9%
c800
 
5.0%
p753
 
4.7%
n747
 
4.7%
l746
 
4.7%
Other values (13)5086
32.1%
Distinct49
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:49.320404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length28
Median length22
Mean length16.91117691
Min length8

Characters and Unicode

Total characters45694
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHPE NF5280M6
2nd rowInspur ThinkSystem SR650
3rd rowFujitsu UCS C240
4th rowSuperMicro ThinkSystem SR650
5th rowInspur NF5280M6
ValueCountFrequency (%)
thinksystem419
 
6.0%
sr650419
 
6.0%
hpe413
 
5.9%
supermicro413
 
5.9%
lenovo407
 
5.9%
nf5280m6407
 
5.9%
c240398
 
5.7%
ucs398
 
5.7%
122b388
 
5.6%
cisco387
 
5.6%
Other values (8)2901
41.7%
2025-12-13T12:18:49.851894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4248
 
9.3%
02314
 
5.1%
o1977
 
4.3%
i1951
 
4.3%
21947
 
4.3%
R1878
 
4.1%
S1649
 
3.6%
e1612
 
3.5%
r1529
 
3.3%
n1529
 
3.3%
Other values (36)25060
54.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)45694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4248
 
9.3%
02314
 
5.1%
o1977
 
4.3%
i1951
 
4.3%
21947
 
4.3%
R1878
 
4.1%
S1649
 
3.6%
e1612
 
3.5%
r1529
 
3.3%
n1529
 
3.3%
Other values (36)25060
54.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4248
 
9.3%
02314
 
5.1%
o1977
 
4.3%
i1951
 
4.3%
21947
 
4.3%
R1878
 
4.1%
S1649
 
3.6%
e1612
 
3.5%
r1529
 
3.3%
n1529
 
3.3%
Other values (36)25060
54.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4248
 
9.3%
02314
 
5.1%
o1977
 
4.3%
i1951
 
4.3%
21947
 
4.3%
R1878
 
4.1%
S1649
 
3.6%
e1612
 
3.5%
r1529
 
3.3%
n1529
 
3.3%
Other values (36)25060
54.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:50.025223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.005181347
Min length3

Characters and Unicode

Total characters10822
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntel
2nd rowIntel
3rd rowIntel
4th rowAMD
5th rowAMD
ValueCountFrequency (%)
intel1358
50.3%
amd1344
49.7%
2025-12-13T12:18:50.397347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I1358
12.5%
n1358
12.5%
t1358
12.5%
e1358
12.5%
l1358
12.5%
A1344
12.4%
M1344
12.4%
D1344
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I1358
12.5%
n1358
12.5%
t1358
12.5%
e1358
12.5%
l1358
12.5%
A1344
12.4%
M1344
12.4%
D1344
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I1358
12.5%
n1358
12.5%
t1358
12.5%
e1358
12.5%
l1358
12.5%
A1344
12.4%
M1344
12.4%
D1344
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I1358
12.5%
n1358
12.5%
t1358
12.5%
e1358
12.5%
l1358
12.5%
A1344
12.4%
M1344
12.4%
D1344
12.4%
Distinct2594
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:50.780327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters16212
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2490 ?
Unique (%)92.2%

Sample

1st rowNV1237
2nd rowPM7222
3rd rowXG6759
4th rowPM4993
5th rowPM4398
ValueCountFrequency (%)
mt90674
 
0.1%
pm27703
 
0.1%
nv74113
 
0.1%
nv53322
 
0.1%
nv28172
 
0.1%
pm97772
 
0.1%
nv89862
 
0.1%
xg24072
 
0.1%
nv34072
 
0.1%
mt32362
 
0.1%
Other values (2584)2678
99.1%
2025-12-13T12:18:51.421878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M1398
 
8.6%
91159
 
7.1%
71145
 
7.1%
41144
 
7.1%
11124
 
6.9%
61107
 
6.8%
51099
 
6.8%
21091
 
6.7%
31069
 
6.6%
81064
 
6.6%
Other values (7)4812
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)16212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M1398
 
8.6%
91159
 
7.1%
71145
 
7.1%
41144
 
7.1%
11124
 
6.9%
61107
 
6.8%
51099
 
6.8%
21091
 
6.7%
31069
 
6.6%
81064
 
6.6%
Other values (7)4812
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M1398
 
8.6%
91159
 
7.1%
71145
 
7.1%
41144
 
7.1%
11124
 
6.9%
61107
 
6.8%
51099
 
6.8%
21091
 
6.7%
31069
 
6.6%
81064
 
6.6%
Other values (7)4812
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M1398
 
8.6%
91159
 
7.1%
71145
 
7.1%
41144
 
7.1%
11124
 
6.9%
61107
 
6.8%
51099
 
6.8%
21091
 
6.7%
31069
 
6.6%
81064
 
6.6%
Other values (7)4812
29.7%
Distinct2331
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:51.773397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters32424
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1995 ?
Unique (%)73.8%

Sample

1st rowABCD6458EFGH
2nd rowABCD9175EFGH
3rd rowABCD6139EFGH
4th rowABCD8290EFGH
5th rowABCD8437EFGH
ValueCountFrequency (%)
abcd7495efgh4
 
0.1%
abcd3783efgh4
 
0.1%
abcd3149efgh3
 
0.1%
abcd9850efgh3
 
0.1%
abcd4409efgh3
 
0.1%
abcd5151efgh3
 
0.1%
abcd2669efgh3
 
0.1%
abcd8684efgh3
 
0.1%
abcd6021efgh3
 
0.1%
abcd2363efgh3
 
0.1%
Other values (2321)2670
98.8%
2025-12-13T12:18:52.288809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A2702
 
8.3%
E2702
 
8.3%
H2702
 
8.3%
G2702
 
8.3%
B2702
 
8.3%
F2702
 
8.3%
D2702
 
8.3%
C2702
 
8.3%
71180
 
3.6%
51123
 
3.5%
Other values (8)8505
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)32424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A2702
 
8.3%
E2702
 
8.3%
H2702
 
8.3%
G2702
 
8.3%
B2702
 
8.3%
F2702
 
8.3%
D2702
 
8.3%
C2702
 
8.3%
71180
 
3.6%
51123
 
3.5%
Other values (8)8505
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A2702
 
8.3%
E2702
 
8.3%
H2702
 
8.3%
G2702
 
8.3%
B2702
 
8.3%
F2702
 
8.3%
D2702
 
8.3%
C2702
 
8.3%
71180
 
3.6%
51123
 
3.5%
Other values (8)8505
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A2702
 
8.3%
E2702
 
8.3%
H2702
 
8.3%
G2702
 
8.3%
B2702
 
8.3%
F2702
 
8.3%
D2702
 
8.3%
C2702
 
8.3%
71180
 
3.6%
51123
 
3.5%
Other values (8)8505
26.2%
Distinct869
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:52.672186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters24318
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)5.0%

Sample

1st rowD3V874VAB
2nd rowD3V566VAB
3rd rowD3V322VAB
4th rowD3V877VAB
5th rowD3V400VAB
ValueCountFrequency (%)
d3v290vab10
 
0.4%
d3v137vab10
 
0.4%
d3v924vab8
 
0.3%
d3v715vab8
 
0.3%
d3v954vab8
 
0.3%
d3v921vab8
 
0.3%
d3v100vab8
 
0.3%
d3v334vab8
 
0.3%
d3v852vab8
 
0.3%
d3v598vab8
 
0.3%
Other values (859)2618
96.9%
2025-12-13T12:18:53.246590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
V5404
22.2%
33530
14.5%
D2702
11.1%
A2702
11.1%
B2702
11.1%
2904
 
3.7%
7858
 
3.5%
9845
 
3.5%
5832
 
3.4%
4830
 
3.4%
Other values (4)3009
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)24318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V5404
22.2%
33530
14.5%
D2702
11.1%
A2702
11.1%
B2702
11.1%
2904
 
3.7%
7858
 
3.5%
9845
 
3.5%
5832
 
3.4%
4830
 
3.4%
Other values (4)3009
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V5404
22.2%
33530
14.5%
D2702
11.1%
A2702
11.1%
B2702
11.1%
2904
 
3.7%
7858
 
3.5%
9845
 
3.5%
5832
 
3.4%
4830
 
3.4%
Other values (4)3009
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V5404
22.2%
33530
14.5%
D2702
11.1%
A2702
11.1%
B2702
11.1%
2904
 
3.7%
7858
 
3.5%
9845
 
3.5%
5832
 
3.4%
4830
 
3.4%
Other values (4)3009
12.4%
Distinct2651
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:53.700574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters16212
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2600 ?
Unique (%)96.2%

Sample

1st row0x7CCA
2nd row0xD159
3rd row0xD795
4th row0x6E14
5th row0x3C05
ValueCountFrequency (%)
0xb3562
 
0.1%
0xbdce2
 
0.1%
0x0b2f2
 
0.1%
0xae0b2
 
0.1%
0xef402
 
0.1%
0x94712
 
0.1%
0x2e2a2
 
0.1%
0xa4f32
 
0.1%
0x667d2
 
0.1%
0x342b2
 
0.1%
Other values (2641)2682
99.3%
2025-12-13T12:18:54.404427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
03390
20.9%
x2702
16.7%
3729
 
4.5%
A695
 
4.3%
F695
 
4.3%
C694
 
4.3%
9690
 
4.3%
4682
 
4.2%
2679
 
4.2%
5676
 
4.2%
Other values (7)4580
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)16212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03390
20.9%
x2702
16.7%
3729
 
4.5%
A695
 
4.3%
F695
 
4.3%
C694
 
4.3%
9690
 
4.3%
4682
 
4.2%
2679
 
4.2%
5676
 
4.2%
Other values (7)4580
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03390
20.9%
x2702
16.7%
3729
 
4.5%
A695
 
4.3%
F695
 
4.3%
C694
 
4.3%
9690
 
4.3%
4682
 
4.2%
2679
 
4.2%
5676
 
4.2%
Other values (7)4580
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03390
20.9%
x2702
16.7%
3729
 
4.5%
A695
 
4.3%
F695
 
4.3%
C694
 
4.3%
9690
 
4.3%
4682
 
4.2%
2679
 
4.2%
5676
 
4.2%
Other values (7)4580
28.3%
Distinct2652
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:54.803172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters16212
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2602 ?
Unique (%)96.3%

Sample

1st row0xEE76
2nd row0xB954
3rd row0xAA21
4th row0x382C
5th row0xF56B
ValueCountFrequency (%)
0x01262
 
0.1%
0xc41a2
 
0.1%
0x4c4b2
 
0.1%
0xa7732
 
0.1%
0xd6832
 
0.1%
0xaf762
 
0.1%
0x53392
 
0.1%
0x07ff2
 
0.1%
0x2e332
 
0.1%
0x6de42
 
0.1%
Other values (2642)2682
99.3%
2025-12-13T12:18:55.349298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
03363
20.7%
x2702
16.7%
1711
 
4.4%
4703
 
4.3%
D696
 
4.3%
7685
 
4.2%
5685
 
4.2%
B680
 
4.2%
A679
 
4.2%
F672
 
4.1%
Other values (7)4636
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)16212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03363
20.7%
x2702
16.7%
1711
 
4.4%
4703
 
4.3%
D696
 
4.3%
7685
 
4.2%
5685
 
4.2%
B680
 
4.2%
A679
 
4.2%
F672
 
4.1%
Other values (7)4636
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03363
20.7%
x2702
16.7%
1711
 
4.4%
4703
 
4.3%
D696
 
4.3%
7685
 
4.2%
5685
 
4.2%
B680
 
4.2%
A679
 
4.2%
F672
 
4.1%
Other values (7)4636
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03363
20.7%
x2702
16.7%
1711
 
4.4%
4703
 
4.3%
D696
 
4.3%
7685
 
4.2%
5685
 
4.2%
B680
 
4.2%
A679
 
4.2%
F672
 
4.1%
Other values (7)4636
28.6%

nvme_capacity_(tb)
Real number (ℝ)

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.02316802
Minimum1.8
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:55.520718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile1.8
Q13.8
median15.3
Q360
95-th percentile256
Maximum256
Range254.2
Interquartile range (IQR)56.2

Descriptive statistics

Standard deviation76.51364877
Coefficient of variation (CV)1.470761041
Kurtosis2.232356809
Mean52.02316802
Median Absolute Deviation (MAD)11.9
Skewness1.865476574
Sum140566.6
Variance5854.338449
MonotonicityNot monotonic
2025-12-13T12:18:55.671993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.8287
10.6%
15.3284
10.5%
7.6283
10.5%
25.4278
10.3%
3.8272
10.1%
256272
10.1%
30.6262
9.7%
122260
9.6%
60254
9.4%
3.4250
9.3%
ValueCountFrequency (%)
1.8287
10.6%
3.4250
9.3%
3.8272
10.1%
7.6283
10.5%
15.3284
10.5%
ValueCountFrequency (%)
256272
10.1%
122260
9.6%
60254
9.4%
30.6262
9.7%
25.4278
10.3%

ieee_oui_identifier
Text

Unique 

Distinct2702
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:56.043301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.007401925
Min length2

Characters and Unicode

Total characters16232
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2702 ?
Unique (%)100.0%

Sample

1st rowE594C5
2nd row449C7E
3rd row99B95E
4th rowC1C184
5th rowB8F6D8
ValueCountFrequency (%)
e594c51
 
< 0.1%
093b751
 
< 0.1%
c54f321
 
< 0.1%
5604b51
 
< 0.1%
99b95e1
 
< 0.1%
c1c1841
 
< 0.1%
b8f6d81
 
< 0.1%
5a86a81
 
< 0.1%
d05a9e1
 
< 0.1%
d145de1
 
< 0.1%
Other values (2692)2692
99.6%
2025-12-13T12:18:56.609919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F1056
 
6.5%
D1040
 
6.4%
A1034
 
6.4%
71034
 
6.4%
C1033
 
6.4%
E1031
 
6.4%
41031
 
6.4%
11029
 
6.3%
61024
 
6.3%
21021
 
6.3%
Other values (8)5899
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)16232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F1056
 
6.5%
D1040
 
6.4%
A1034
 
6.4%
71034
 
6.4%
C1033
 
6.4%
E1031
 
6.4%
41031
 
6.4%
11029
 
6.3%
61024
 
6.3%
21021
 
6.3%
Other values (8)5899
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F1056
 
6.5%
D1040
 
6.4%
A1034
 
6.4%
71034
 
6.4%
C1033
 
6.4%
E1031
 
6.4%
41031
 
6.4%
11029
 
6.3%
61024
 
6.3%
21021
 
6.3%
Other values (8)5899
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F1056
 
6.5%
D1040
 
6.4%
A1034
 
6.4%
71034
 
6.4%
C1033
 
6.4%
E1031
 
6.4%
41031
 
6.4%
11029
 
6.3%
61024
 
6.3%
21021
 
6.3%
Other values (8)5899
36.3%
Distinct30
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:56.862058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters13510
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0.9
2nd row1.0.5
3rd row1.1.5
4th row1.2.3
5th row1.0.9
ValueCountFrequency (%)
1.2.3125
 
4.6%
1.1.7108
 
4.0%
1.1.2105
 
3.9%
1.1.599
 
3.7%
1.1.498
 
3.6%
1.1.097
 
3.6%
1.0.897
 
3.6%
1.2.896
 
3.6%
1.2.596
 
3.6%
1.2.496
 
3.6%
Other values (20)1685
62.4%
2025-12-13T12:18:57.243463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.5404
40.0%
13889
28.8%
21186
 
8.8%
01134
 
8.4%
3295
 
2.2%
7279
 
2.1%
5278
 
2.1%
8274
 
2.0%
6270
 
2.0%
4267
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)13510
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.5404
40.0%
13889
28.8%
21186
 
8.8%
01134
 
8.4%
3295
 
2.2%
7279
 
2.1%
5278
 
2.1%
8274
 
2.0%
6270
 
2.0%
4267
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13510
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.5404
40.0%
13889
28.8%
21186
 
8.8%
01134
 
8.4%
3295
 
2.2%
7279
 
2.1%
5278
 
2.1%
8274
 
2.0%
6270
 
2.0%
4267
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13510
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.5404
40.0%
13889
28.8%
21186
 
8.8%
01134
 
8.4%
3295
 
2.2%
7279
 
2.1%
5278
 
2.1%
8274
 
2.0%
6270
 
2.0%
4267
 
2.0%

nvme_capacity_(tb).1
Real number (ℝ)

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.02316802
Minimum1.8
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:57.396681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile1.8
Q13.8
median15.3
Q360
95-th percentile256
Maximum256
Range254.2
Interquartile range (IQR)56.2

Descriptive statistics

Standard deviation76.51364877
Coefficient of variation (CV)1.470761041
Kurtosis2.232356809
Mean52.02316802
Median Absolute Deviation (MAD)11.9
Skewness1.865476574
Sum140566.6
Variance5854.338449
MonotonicityNot monotonic
2025-12-13T12:18:57.537864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.8287
10.6%
15.3284
10.5%
7.6283
10.5%
25.4278
10.3%
3.8272
10.1%
256272
10.1%
30.6262
9.7%
122260
9.6%
60254
9.4%
3.4250
9.3%
ValueCountFrequency (%)
1.8287
10.6%
3.4250
9.3%
3.8272
10.1%
7.6283
10.5%
15.3284
10.5%
ValueCountFrequency (%)
256272
10.1%
122260
9.6%
60254
9.4%
30.6262
9.7%
25.4278
10.3%

unallocated
Real number (ℝ)

Unique 

Distinct2702
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246371936.8
Minimum69103
Maximum499939974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:57.709188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum69103
5-th percentile22923079
Q1121991877.2
median245121665.5
Q3371748903.8
95-th percentile473089182.8
Maximum499939974
Range499870871
Interquartile range (IQR)249757026.5

Descriptive statistics

Standard deviation143923585.1
Coefficient of variation (CV)0.5841719919
Kurtosis-1.192793703
Mean246371936.8
Median Absolute Deviation (MAD)124937097
Skewness0.02353684345
Sum6.656969733 × 1011
Variance2.071399834 × 1016
MonotonicityNot monotonic
2025-12-13T12:18:58.012177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3994461761
 
< 0.1%
4254102001
 
< 0.1%
3443025911
 
< 0.1%
4914782811
 
< 0.1%
4857867131
 
< 0.1%
3527614861
 
< 0.1%
3328131211
 
< 0.1%
3813054591
 
< 0.1%
1739065391
 
< 0.1%
74869641
 
< 0.1%
Other values (2692)2692
99.6%
ValueCountFrequency (%)
691031
< 0.1%
1969531
< 0.1%
3470941
< 0.1%
5243701
< 0.1%
5553591
< 0.1%
ValueCountFrequency (%)
4999399741
< 0.1%
4995302891
< 0.1%
4993481061
< 0.1%
4993066701
< 0.1%
4992253671
< 0.1%

controller_count
Real number (ℝ)

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.473353072
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:58.151210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.104596673
Coefficient of variation (CV)0.4465988644
Kurtosis-1.327533434
Mean2.473353072
Median Absolute Deviation (MAD)1
Skewness0.03532623411
Sum6683
Variance1.22013381
MonotonicityNot monotonic
2025-12-13T12:18:58.277193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2709
26.2%
3682
25.2%
1675
25.0%
4636
23.5%
ValueCountFrequency (%)
1675
25.0%
2709
26.2%
3682
25.2%
4636
23.5%
ValueCountFrequency (%)
4636
23.5%
3682
25.2%
2709
26.2%
1675
25.0%

nvme_version
Real number (ℝ)

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.56569208
Minimum1.3
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:58.408739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.3
Q11.3
median1.4
Q32
95-th percentile2
Maximum2
Range0.7
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.3091889378
Coefficient of variation (CV)0.1974774873
Kurtosis-1.49447107
Mean1.56569208
Median Absolute Deviation (MAD)0.1
Skewness0.6570360415
Sum4230.5
Variance0.09559779926
MonotonicityNot monotonic
2025-12-13T12:18:58.540250image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1.3911
33.7%
2898
33.2%
1.4893
33.0%
ValueCountFrequency (%)
1.3911
33.7%
1.4893
33.0%
2898
33.2%
ValueCountFrequency (%)
2898
33.2%
1.4893
33.0%
1.3911
33.7%

namespace_count
Real number (ℝ)

Distinct32
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.43560326
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:58.709610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q325
95-th percentile31
Maximum32
Range31
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.310997088
Coefficient of variation (CV)0.5665138628
Kurtosis-1.24087528
Mean16.43560326
Median Absolute Deviation (MAD)8
Skewness0.005298684884
Sum44409
Variance86.69466678
MonotonicityNot monotonic
2025-12-13T12:18:58.878975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
4108
 
4.0%
27100
 
3.7%
399
 
3.7%
696
 
3.6%
3096
 
3.6%
1594
 
3.5%
1393
 
3.4%
2591
 
3.4%
3289
 
3.3%
2689
 
3.3%
Other values (22)1747
64.7%
ValueCountFrequency (%)
179
2.9%
280
3.0%
399
3.7%
4108
4.0%
574
2.7%
ValueCountFrequency (%)
3289
3.3%
3165
2.4%
3096
3.6%
2983
3.1%
2882
3.0%

namespace_size
Real number (ℝ)

Distinct2451
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7116.106588
Minimum131
Maximum13997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:59.041762image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum131
5-th percentile868.45
Q13702
median7233
Q310504.5
95-th percentile13250.7
Maximum13997
Range13866
Interquartile range (IQR)6802.5

Descriptive statistics

Standard deviation3944.632832
Coefficient of variation (CV)0.5543245851
Kurtosis-1.171035146
Mean7116.106588
Median Absolute Deviation (MAD)3398.5
Skewness-0.04532780013
Sum19227720
Variance15560128.18
MonotonicityNot monotonic
2025-12-13T12:18:59.226740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123093
 
0.1%
96113
 
0.1%
31713
 
0.1%
80283
 
0.1%
46453
 
0.1%
121863
 
0.1%
98753
 
0.1%
71753
 
0.1%
83603
 
0.1%
98823
 
0.1%
Other values (2441)2672
98.9%
ValueCountFrequency (%)
1311
< 0.1%
1502
0.1%
1691
< 0.1%
1711
< 0.1%
1741
< 0.1%
ValueCountFrequency (%)
139971
< 0.1%
139931
< 0.1%
139691
< 0.1%
139681
< 0.1%
139671
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1882.527017
Minimum512
Maximum4096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:18:59.380475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum512
5-th percentile512
Q1512
median1092
Q34096
95-th percentile4096
Maximum4096
Range3584
Interquartile range (IQR)3584

Descriptive statistics

Standard deviation1570.902837
Coefficient of variation (CV)0.8344649625
Kurtosis-1.478839675
Mean1882.527017
Median Absolute Deviation (MAD)580
Skewness0.652710268
Sum5086588
Variance2467735.722
MonotonicityNot monotonic
2025-12-13T12:18:59.527628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
512932
34.5%
4096891
33.0%
1092879
32.5%
ValueCountFrequency (%)
512932
34.5%
1092879
32.5%
4096891
33.0%
ValueCountFrequency (%)
4096891
33.0%
1092879
32.5%
512932
34.5%

nand_type
Unsupported

Missing  Rejected  Unsupported 

Missing2702
Missing (%)100.0%
Memory size21.2 KiB
Distinct947
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
Minimum2023-01-01 00:00:00+00:00
Maximum2025-12-28 00:00:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-13T12:18:59.696995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-12-13T12:18:59.881977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temperature_warning
Real number (ℝ)

Distinct31
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.97853442
Minimum60
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:00.066920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile61
Q167
median75
Q383
95-th percentile89
Maximum90
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.952851841
Coefficient of variation (CV)0.1194055327
Kurtosis-1.212168686
Mean74.97853442
Median Absolute Deviation (MAD)8
Skewness0.01325559728
Sum202592
Variance80.15355609
MonotonicityNot monotonic
2025-12-13T12:19:00.229732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
68106
 
3.9%
72106
 
3.9%
81100
 
3.7%
6599
 
3.7%
9098
 
3.6%
8398
 
3.6%
8498
 
3.6%
6994
 
3.5%
6391
 
3.4%
7990
 
3.3%
Other values (21)1722
63.7%
ValueCountFrequency (%)
6087
3.2%
6185
3.1%
6279
2.9%
6391
3.4%
6486
3.2%
ValueCountFrequency (%)
9098
3.6%
8988
3.3%
8871
2.6%
8785
3.1%
8686
3.2%

temperature_critical
Real number (ℝ)

Distinct21
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.99555885
Minimum80
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:00.383473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile81
Q185
median90
Q395
95-th percentile99
Maximum100
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.065602934
Coefficient of variation (CV)0.06739891403
Kurtosis-1.213534422
Mean89.99555885
Median Absolute Deviation (MAD)5
Skewness0.009350570977
Sum243168
Variance36.79153895
MonotonicityNot monotonic
2025-12-13T12:19:00.546251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
88146
 
5.4%
83142
 
5.3%
98138
 
5.1%
84135
 
5.0%
100135
 
5.0%
96134
 
5.0%
97133
 
4.9%
91133
 
4.9%
86133
 
4.9%
82130
 
4.8%
Other values (11)1343
49.7%
ValueCountFrequency (%)
80124
4.6%
81127
4.7%
82130
4.8%
83142
5.3%
84135
5.0%
ValueCountFrequency (%)
100135
5.0%
99116
4.3%
98138
5.1%
97133
4.9%
96134
5.0%

temperature_current
Real number (ℝ)

Distinct41
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.89600296
Minimum30
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:00.731235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile31
Q139
median50
Q360
95-th percentile68
Maximum70
Range40
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.01122172
Coefficient of variation (CV)0.2407251284
Kurtosis-1.237535416
Mean49.89600296
Median Absolute Deviation (MAD)11
Skewness-0.01114307579
Sum134819
Variance144.2694472
MonotonicityNot monotonic
2025-12-13T12:19:00.916190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
3482
 
3.0%
3082
 
3.0%
6582
 
3.0%
3881
 
3.0%
5481
 
3.0%
5874
 
2.7%
3672
 
2.7%
5672
 
2.7%
6672
 
2.7%
3371
 
2.6%
Other values (31)1933
71.5%
ValueCountFrequency (%)
3082
3.0%
3167
2.5%
3264
2.4%
3371
2.6%
3482
3.0%
ValueCountFrequency (%)
7063
2.3%
6964
2.4%
6867
2.5%
6763
2.3%
6672
2.7%

available_spare
Real number (ℝ)

Distinct101
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.9900074
Minimum0
Maximum100
Zeros23
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:01.101137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median51
Q375
95-th percentile95
Maximum100
Range100
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.14074116
Coefficient of variation (CV)0.5829313232
Kurtosis-1.206708204
Mean49.9900074
Median Absolute Deviation (MAD)25
Skewness-0.01975303007
Sum135073
Variance849.1827953
MonotonicityNot monotonic
2025-12-13T12:19:01.286124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143
 
1.6%
1739
 
1.4%
3237
 
1.4%
6236
 
1.3%
5236
 
1.3%
3836
 
1.3%
2135
 
1.3%
5434
 
1.3%
8634
 
1.3%
9233
 
1.2%
Other values (91)2339
86.6%
ValueCountFrequency (%)
023
0.9%
143
1.6%
226
1.0%
331
1.1%
421
0.8%
ValueCountFrequency (%)
10022
0.8%
9929
1.1%
9827
1.0%
9718
0.7%
9624
0.9%

percentage_used
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.51369356
Minimum0
Maximum100
Zeros29
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:01.471061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q126
median51
Q375
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.85697982
Coefficient of variation (CV)0.5712704375
Kurtosis-1.169817927
Mean50.51369356
Median Absolute Deviation (MAD)25
Skewness-0.02070815554
Sum136488
Variance832.7252845
MonotonicityNot monotonic
2025-12-13T12:19:01.649501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8243
 
1.6%
6940
 
1.5%
7138
 
1.4%
6436
 
1.3%
736
 
1.3%
6735
 
1.3%
2235
 
1.3%
2634
 
1.3%
4533
 
1.2%
5833
 
1.2%
Other values (91)2339
86.6%
ValueCountFrequency (%)
029
1.1%
125
0.9%
221
0.8%
327
1.0%
421
0.8%
ValueCountFrequency (%)
10031
1.1%
9927
1.0%
9828
1.0%
9730
1.1%
9630
1.1%

optional_features
Unsupported

Missing  Rejected  Unsupported 

Missing2702
Missing (%)100.0%
Memory size21.2 KiB

log_page_support
Unsupported

Missing  Rejected  Unsupported 

Missing2702
Missing (%)100.0%
Memory size21.2 KiB

max_i_o_pages
Real number (ℝ)

Distinct447
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.6550703
Minimum64
Maximum512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:01.834487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile88
Q1176
median294
Q3406
95-th percentile493
Maximum512
Range448
Interquartile range (IQR)230

Descriptive statistics

Standard deviation130.9132965
Coefficient of variation (CV)0.4488634343
Kurtosis-1.219982959
Mean291.6550703
Median Absolute Deviation (MAD)116
Skewness-0.03410422131
Sum788052
Variance17138.2912
MonotonicityNot monotonic
2025-12-13T12:19:02.019457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11713
 
0.5%
10012
 
0.4%
12812
 
0.4%
41812
 
0.4%
17612
 
0.4%
47412
 
0.4%
9912
 
0.4%
21612
 
0.4%
46112
 
0.4%
25311
 
0.4%
Other values (437)2582
95.6%
ValueCountFrequency (%)
643
0.1%
656
0.2%
663
0.1%
676
0.2%
686
0.2%
ValueCountFrequency (%)
5128
0.3%
5119
0.3%
5106
0.2%
5096
0.2%
5086
0.2%

available_spare.1
Unsupported

Missing  Rejected  Unsupported 

Missing2702
Missing (%)100.0%
Memory size21.2 KiB

available_spare_threshold
Unsupported

Missing  Rejected  Unsupported 

Missing2702
Missing (%)100.0%
Memory size21.2 KiB

percentage_used.1
Unsupported

Missing  Rejected  Unsupported 

Missing2702
Missing (%)100.0%
Memory size21.2 KiB

data_units_read
Unsupported

Missing  Rejected  Unsupported 

Missing2702
Missing (%)100.0%
Memory size21.2 KiB

data_units_written
Unsupported

Missing  Rejected  Unsupported 

Missing2702
Missing (%)100.0%
Memory size21.2 KiB

host_read_commands
Real number (ℝ)

Distinct498
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.49488527 × 1011
Minimum2 × 1011
Maximum7 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:02.336795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2 × 1011
5-th percentile2.4205 × 1011
Q13.37 × 1011
median4.48 × 1011
Q35.6 × 1011
95-th percentile6.58 × 1011
Maximum7 × 1011
Range5 × 1011
Interquartile range (IQR)2.23 × 1011

Descriptive statistics

Standard deviation1.326222992 × 1011
Coefficient of variation (CV)0.295051578
Kurtosis-1.09888642
Mean4.49488527 × 1011
Median Absolute Deviation (MAD)1.11 × 1011
Skewness0.01068906591
Sum1.214518 × 1015
Variance1.758867425 × 1022
MonotonicityNot monotonic
2025-12-13T12:19:02.528370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.01 × 101114
 
0.5%
2.69 × 101113
 
0.5%
4.92 × 101113
 
0.5%
3.73 × 101112
 
0.4%
6 × 101112
 
0.4%
5.46 × 101112
 
0.4%
3.93 × 101112
 
0.4%
4.43 × 101111
 
0.4%
5.23 × 101111
 
0.4%
5.43 × 101111
 
0.4%
Other values (488)2581
95.5%
ValueCountFrequency (%)
2 × 10111
 
< 0.1%
2.01 × 10111
 
< 0.1%
2.02 × 10112
 
0.1%
2.03 × 10112
 
0.1%
2.04 × 10115
0.2%
ValueCountFrequency (%)
7 × 10112
 
0.1%
6.99 × 10112
 
0.1%
6.98 × 10112
 
0.1%
6.97 × 10115
0.2%
6.96 × 10112
 
0.1%

host_write_commands
Real number (ℝ)

Distinct543
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.270629164 × 1011
Minimum1.63 × 1011
Maximum7.66 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:02.709765image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.63 × 1011
5-th percentile2.2605 × 1011
Q13.2 × 1011
median4.215 × 1011
Q35.28 × 1011
95-th percentile6.5 × 1011
Maximum7.66 × 1011
Range6.03 × 1011
Interquartile range (IQR)2.08 × 1011

Descriptive statistics

Standard deviation1.324540944 × 1011
Coefficient of variation (CV)0.3101512431
Kurtosis-0.8428665856
Mean4.270629164 × 1011
Median Absolute Deviation (MAD)1.04 × 1011
Skewness0.1769332596
Sum1.153924 × 1015
Variance1.754408712 × 1022
MonotonicityNot monotonic
2025-12-13T12:19:02.891345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.02 × 101115
 
0.6%
3.64 × 101113
 
0.5%
2.87 × 101113
 
0.5%
4.3 × 101113
 
0.5%
4.68 × 101112
 
0.4%
5.27 × 101112
 
0.4%
3.84 × 101112
 
0.4%
4.73 × 101112
 
0.4%
4.29 × 101112
 
0.4%
4.9 × 101112
 
0.4%
Other values (533)2576
95.3%
ValueCountFrequency (%)
1.63 × 10111
< 0.1%
1.67 × 10111
< 0.1%
1.69 × 10111
< 0.1%
1.7 × 10111
< 0.1%
1.71 × 10111
< 0.1%
ValueCountFrequency (%)
7.66 × 10111
< 0.1%
7.62 × 10111
< 0.1%
7.55 × 10111
< 0.1%
7.49 × 10111
< 0.1%
7.46 × 10111
< 0.1%

controller_busy_time
Real number (ℝ)

Distinct2606
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41686.78053
Minimum20000
Maximum64983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:03.073080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile24248.45
Q132523
median40810
Q351326.25
95-th percentile60389.9
Maximum64983
Range44983
Interquartile range (IQR)18803.25

Descriptive statistics

Standard deviation11423.17118
Coefficient of variation (CV)0.2740238281
Kurtosis-1.000782811
Mean41686.78053
Median Absolute Deviation (MAD)9234.5
Skewness0.1422528685
Sum112637681
Variance130488839.9
MonotonicityNot monotonic
2025-12-13T12:19:03.254672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
354813
 
0.1%
362693
 
0.1%
298923
 
0.1%
507852
 
0.1%
526142
 
0.1%
483452
 
0.1%
462402
 
0.1%
336772
 
0.1%
378672
 
0.1%
355452
 
0.1%
Other values (2596)2679
99.1%
ValueCountFrequency (%)
200001
< 0.1%
201141
< 0.1%
201781
< 0.1%
202231
< 0.1%
202431
< 0.1%
ValueCountFrequency (%)
649831
< 0.1%
649391
< 0.1%
648541
< 0.1%
648071
< 0.1%
648001
< 0.1%

power_cycles
Real number (ℝ)

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.839008142
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:03.405870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q16
median9
Q312
95-th percentile15
Maximum15
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.782545728
Coefficient of variation (CV)0.4279378033
Kurtosis-1.2179902
Mean8.839008142
Median Absolute Deviation (MAD)3
Skewness0.05303585831
Sum23883
Variance14.30765218
MonotonicityNot monotonic
2025-12-13T12:19:03.536904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3245
9.1%
9219
 
8.1%
6215
 
8.0%
5215
 
8.0%
7209
 
7.7%
4207
 
7.7%
15207
 
7.7%
14203
 
7.5%
8201
 
7.4%
11200
 
7.4%
Other values (3)581
21.5%
ValueCountFrequency (%)
3245
9.1%
4207
7.7%
5215
8.0%
6215
8.0%
7209
7.7%
ValueCountFrequency (%)
15207
7.7%
14203
7.5%
13192
7.1%
12190
7.0%
11200
7.4%

power_on_hours
Real number (ℝ)

Distinct1766
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2058.881939
Minimum500
Maximum3997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:03.708413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile733.15
Q11390.25
median1977
Q32735
95-th percentile3598.95
Maximum3997
Range3497
Interquartile range (IQR)1344.75

Descriptive statistics

Standard deviation848.8776189
Coefficient of variation (CV)0.4123002892
Kurtosis-0.7589602347
Mean2058.881939
Median Absolute Deviation (MAD)659
Skewness0.23134966
Sum5563099
Variance720593.2119
MonotonicityNot monotonic
2025-12-13T12:19:03.903430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12226
 
0.2%
14405
 
0.2%
15905
 
0.2%
28205
 
0.2%
15295
 
0.2%
19105
 
0.2%
18665
 
0.2%
15865
 
0.2%
15135
 
0.2%
14244
 
0.1%
Other values (1756)2652
98.1%
ValueCountFrequency (%)
5001
< 0.1%
5012
0.1%
5041
< 0.1%
5061
< 0.1%
5071
< 0.1%
ValueCountFrequency (%)
39972
0.1%
39921
< 0.1%
39811
< 0.1%
39802
0.1%
39712
0.1%

unsafe_shutdowns
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.517764619
Minimum0
Maximum5
Zeros446
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:04.066631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.714827134
Coefficient of variation (CV)0.6810911238
Kurtosis-1.280557642
Mean2.517764619
Median Absolute Deviation (MAD)2
Skewness-0.009060086114
Sum6803
Variance2.940632099
MonotonicityNot monotonic
2025-12-13T12:19:04.204255image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5463
17.1%
4459
17.0%
2454
16.8%
1448
16.6%
0446
16.5%
3432
16.0%
ValueCountFrequency (%)
0446
16.5%
1448
16.6%
2454
16.8%
3432
16.0%
4459
17.0%
ValueCountFrequency (%)
5463
17.1%
4459
17.0%
3432
16.0%
2454
16.8%
1448
16.6%

media_and_data_integrity_errors
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros2702
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:04.320147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2025-12-13T12:19:04.436072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
02702
100.0%
ValueCountFrequency (%)
02702
100.0%
ValueCountFrequency (%)
02702
100.0%

error_information_log_entries
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros2702
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:04.551985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2025-12-13T12:19:04.683532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
02702
100.0%
ValueCountFrequency (%)
02702
100.0%
ValueCountFrequency (%)
02702
100.0%

warning__comp._temperature_time
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros2702
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:04.805997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2025-12-13T12:19:04.921910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
02702
100.0%
ValueCountFrequency (%)
02702
100.0%
ValueCountFrequency (%)
02702
100.0%

critical_comp._temperature_time
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros2702
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:05.037805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2025-12-13T12:19:05.169348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
02702
100.0%
ValueCountFrequency (%)
02702
100.0%
ValueCountFrequency (%)
02702
100.0%

ff
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:05.307467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.499259808
Min length3

Characters and Unicode

Total characters9455
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE3.s
2nd rowE3.s
3rd rowE3.s
4th rowU.2
5th rowE3.s
ValueCountFrequency (%)
u.21353
50.1%
e3.s1349
49.9%
2025-12-13T12:19:05.623953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.2702
28.6%
U1353
14.3%
21353
14.3%
E1349
14.3%
31349
14.3%
s1349
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)9455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.2702
28.6%
U1353
14.3%
21353
14.3%
E1349
14.3%
31349
14.3%
s1349
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.2702
28.6%
U1353
14.3%
21353
14.3%
E1349
14.3%
31349
14.3%
s1349
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.2702
28.6%
U1353
14.3%
21353
14.3%
E1349
14.3%
31349
14.3%
s1349
14.3%

read_latency_(ms)
Real number (ℝ)

Distinct1475
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.94819023
Minimum10.01
Maximum29.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:05.808877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10.01
5-th percentile10.9405
Q114.99
median19.92
Q324.8275
95-th percentile28.9595
Maximum29.99
Range19.98
Interquartile range (IQR)9.8375

Descriptive statistics

Standard deviation5.761507438
Coefficient of variation (CV)0.288823566
Kurtosis-1.202717446
Mean19.94819023
Median Absolute Deviation (MAD)4.92
Skewness-0.01221504508
Sum53900.01
Variance33.19496796
MonotonicityNot monotonic
2025-12-13T12:19:06.009475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.456
 
0.2%
18.676
 
0.2%
16.225
 
0.2%
19.745
 
0.2%
18.785
 
0.2%
26.825
 
0.2%
29.265
 
0.2%
24.355
 
0.2%
295
 
0.2%
29.725
 
0.2%
Other values (1465)2650
98.1%
ValueCountFrequency (%)
10.011
 
< 0.1%
10.023
0.1%
10.031
 
< 0.1%
10.052
0.1%
10.062
0.1%
ValueCountFrequency (%)
29.991
 
< 0.1%
29.981
 
< 0.1%
29.974
0.1%
29.962
0.1%
29.951
 
< 0.1%

write_latency_(ms)
Real number (ℝ)

Distinct1785
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.15216506
Minimum20
Maximum49.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2025-12-13T12:19:06.210079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21.57
Q127.52
median35.185
Q342.72
95-th percentile48.44
Maximum49.97
Range29.97
Interquartile range (IQR)15.2

Descriptive statistics

Standard deviation8.652980864
Coefficient of variation (CV)0.2461578355
Kurtosis-1.2058487
Mean35.15216506
Median Absolute Deviation (MAD)7.59
Skewness-0.02575522883
Sum94981.15
Variance74.87407784
MonotonicityNot monotonic
2025-12-13T12:19:06.395031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.556
 
0.2%
31.846
 
0.2%
27.145
 
0.2%
49.045
 
0.2%
38.085
 
0.2%
20.345
 
0.2%
30.464
 
0.1%
30.734
 
0.1%
28.264
 
0.1%
31.964
 
0.1%
Other values (1775)2654
98.2%
ValueCountFrequency (%)
202
0.1%
20.051
 
< 0.1%
20.073
0.1%
20.081
 
< 0.1%
20.12
0.1%
ValueCountFrequency (%)
49.971
 
< 0.1%
49.962
0.1%
49.941
 
< 0.1%
49.913
0.1%
49.91
 
< 0.1%

year
Real number (ℝ)

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023.991488
Minimum2023
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2025-12-13T12:19:06.542243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2023
5-th percentile2023
Q12023
median2024
Q32025
95-th percentile2025
Maximum2025
Range2
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.8101549619
Coefficient of variation (CV)0.0004002758741
Kurtosis-1.47621387
Mean2023.991488
Median Absolute Deviation (MAD)1
Skewness0.01551936461
Sum5468825
Variance0.6563510622
MonotonicityNot monotonic
2025-12-13T12:19:06.695940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2024929
34.4%
2023898
33.2%
2025875
32.4%
ValueCountFrequency (%)
2023898
33.2%
2024929
34.4%
2025875
32.4%
ValueCountFrequency (%)
2025875
32.4%
2024929
34.4%
2023898
33.2%

month
Real number (ℝ)

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.53626943
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2025-12-13T12:19:06.827520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.444054678
Coefficient of variation (CV)0.5269144295
Kurtosis-1.216670616
Mean6.53626943
Median Absolute Deviation (MAD)3
Skewness-0.02500139595
Sum17661
Variance11.86151262
MonotonicityNot monotonic
2025-12-13T12:19:06.974693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4256
9.5%
9251
9.3%
8248
9.2%
10235
8.7%
12227
8.4%
6227
8.4%
1225
8.3%
2218
8.1%
11209
7.7%
3208
7.7%
Other values (2)398
14.7%
ValueCountFrequency (%)
1225
8.3%
2218
8.1%
3208
7.7%
4256
9.5%
5206
7.6%
ValueCountFrequency (%)
12227
8.4%
11209
7.7%
10235
8.7%
9251
9.3%
8248
9.2%